ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Graph Partitioning by Spectral Rounding: Applications in Image Segmentation and Clustering
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Spectral clustering and its use in bioinformatics
Journal of Computational and Applied Mathematics
A survey of kernel and spectral methods for clustering
Pattern Recognition
A tutorial on spectral clustering
Statistics and Computing
Spectral clustering with eigenvector selection
Pattern Recognition
Data Clustering: 50 Years Beyond K-means
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Spectral Clustering in Social Networks
Advances in Web Mining and Web Usage Analysis
Spectral clustering of biological sequence data
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Revealing social networks of spammers through spectral clustering
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Enabling scalable spectral clustering for image segmentation
Pattern Recognition
Spectral clustering for detecting protein complexes in protein-protein interaction (PPI) networks
Mathematical and Computer Modelling: An International Journal
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Spectral clustering has been applied in various applications. But there still exist some important issues to be resolved, among which the two major ones are to (1) specify the scale parameter in calculating the similarity between data objects, and (2) select propoer eigenvectors to reduce data dimensionality. Though these topics have been studied extensively, the existing methods cannot work well in some complicated scenarios, which limits the wide deployment of the spectral clustering method. In this work, we revisit the above two problems and propose three contributions to the field: 1) a unified framework is designed to study the impact of the scale parameter on similarity between data objects. This framework can easily accommodate various state of art spectral clustering methods in determining the scale parameter; 2) a novel approach based on local connectivity analysis is proposed to specify the scale parameter; 3) propose a new method for eigenvector selection. Compared with existing techniques, the proposed approach has a rigorous theoretical basis and is efficient from practical perspective. Experimental results show the efficacy of our approach to clustering data of different scenarios.